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Control and Dynamic Manipulability of a Dual-arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality = = 虚拟现实中用于康复训练的双臂/手机器人外骨骼系统(EXO-UL8)的控制和动力可操作性.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Control and Dynamic Manipulability of a Dual-arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality =/
其他題名:	虚拟现实中用于康复训练的双臂/手机器人外骨骼系统(EXO-UL8)的控制和动力可操作性.
作者:	Shen, Yang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	151 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Contained By:	Dissertations Abstracts International81-03B.
標題:	Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13896793
ISBN:	9781085620178

Control and Dynamic Manipulability of a Dual-arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality = = 虚拟现实中用于康复训练的双臂/手机器人外骨骼系统(EXO-UL8)的控制和动力可操作性.
Shen, Yang.

Control and Dynamic Manipulability of a Dual-arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality =虚拟现实中用于康复训练的双臂/手机器人外骨骼系统(EXO-UL8)的控制和动力可操作性. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 151 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2019.

This item is not available from ProQuest Dissertations & Theses.

Every year there are about 800,000 new stroke patients in the US, and many of them suffer from upper limb neuromuscular disabilities including but not limited to: weakness, spasticity and abnormal synergy. Patients usually have the potential to rehabilitate (to some extent) based on neuroplasticity, and physical therapy intervention helps accelerate the recovery. However, many patients could not afford the expensive physical therapy after the onset of stroke, and miss the opportunity to get recovered. Robot-assisted rehabilitation thus might be the solution, with the following unparalleled advantages: (1) 24/7 capability of human arm gravity compensation; (2) multi-joint movement coordination/correction, which could not be easily done by human physical therapists; (3) dual-arm training, either coupled in joint space or task space; (4) quantitative platform for giving instructions, providing assistance, exerting resistance, and collecting real-time data in kinematics, dynamics and biomechanics; (5) potential training protocol personalization; etc.However, in the rehabilitation robotics field, there are still many open problems. I am especially interested in: (1) compliant control, in high-dimensional multi-joint coordination condition; (2) assist-as-needed (AAN) control, in quantitative model-based approach and model-free approach; (3) dual-arm training, in both symmetric and asymmetric modes; (4) system integration, e.g., virtual reality (VR) serious games and graphical user interfaces (GUIs) design and development.Our dual-arm/hand robotic exoskeleton system, EXO-UL8, is in its 4th generation, with seven (7) arm degrees-of-freedom (DOFs) and one (1) DOF hand gripper enabling hand opening and closing on each side. While developing features on this research platform, I contributed to the robotics research field in the following aspects:(1) I designed and developed a series of eighteen (18) serious VR games and GUIs that could be used for interactive post-stroke rehabilitation training. The VR environment, together with the exoskeleton robot, provides patients and physical therapists a quantitative rehabilitation training platform with capability in real-time human performance data collection and analysis.(2) To provide better compliant control, my colleagues and I proposed and implemented two new admittance controllers, based on the work done by previous research group alumni. Both the hyper parameter-based and Kalman Filter-based admittance controllers have satisfactory heuristic performance, and the latter is more promising in future adaptation. Unlike many other upper-limb exoskeletons, our current system utilizes force and torque (F/T) sensors and position encoders only, no surface electromyography (sEMG) signals are used. It brings convenience to practical use, as well as technical challenges.(3) To provide better AAN control, which is still not well understood in the academia, I worked out a redundant version of modified dynamic manipulability ellipsoid (DME) model to propose an Arm Postural Stability Index (APSI) to quantify the difficulty heterogeneity of the 3D Cartesian workspace. The theoretical framework could be used to teach the exoskeleton where and when to provide assistance, and to guide the virtual reality where to add new minimal challenges to stroke patients. To the best of my knowledge, it is also for the first time that human arm redundancy resolution was investigated when arm gravity is considered.(4) For the first time, my colleagues and I have done a pilot study on asymmetric dual-arm training using the exoskeleton system on one (1) post-stroke patient. The exoskeleton on the healthy side could trigger assistance for that on the affected side, and validates that the current mechanism/control is eligible for asymmetric dual-arm training.(5) Other works of mine include: activities of daily living (ADLs) data visualization for VR game difficulty design; human arm synergy modeling; dual-arm manipulation taxonomy classification (on-going work).

ISBN: 9781085620178Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Control

Control and Dynamic Manipulability of a Dual-arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality = = 虚拟现实中用于康复训练的双臂/手机器人外骨骼系统(EXO-UL8)的控制和动力可操作性.
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245 1 0 $a Control and Dynamic Manipulability of a Dual-arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality = $b 虚拟现实中用于康复训练的双臂/手机器人外骨骼系统(EXO-UL8)的控制和动力可操作性.
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300 $a 151 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
500 $a Advisor: Rosen, Jacob.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2019.
506 $a This item is not available from ProQuest Dissertations & Theses.
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520 $a Every year there are about 800,000 new stroke patients in the US, and many of them suffer from upper limb neuromuscular disabilities including but not limited to: weakness, spasticity and abnormal synergy. Patients usually have the potential to rehabilitate (to some extent) based on neuroplasticity, and physical therapy intervention helps accelerate the recovery. However, many patients could not afford the expensive physical therapy after the onset of stroke, and miss the opportunity to get recovered. Robot-assisted rehabilitation thus might be the solution, with the following unparalleled advantages: (1) 24/7 capability of human arm gravity compensation; (2) multi-joint movement coordination/correction, which could not be easily done by human physical therapists; (3) dual-arm training, either coupled in joint space or task space; (4) quantitative platform for giving instructions, providing assistance, exerting resistance, and collecting real-time data in kinematics, dynamics and biomechanics; (5) potential training protocol personalization; etc.However, in the rehabilitation robotics field, there are still many open problems. I am especially interested in: (1) compliant control, in high-dimensional multi-joint coordination condition; (2) assist-as-needed (AAN) control, in quantitative model-based approach and model-free approach; (3) dual-arm training, in both symmetric and asymmetric modes; (4) system integration, e.g., virtual reality (VR) serious games and graphical user interfaces (GUIs) design and development.Our dual-arm/hand robotic exoskeleton system, EXO-UL8, is in its 4th generation, with seven (7) arm degrees-of-freedom (DOFs) and one (1) DOF hand gripper enabling hand opening and closing on each side. While developing features on this research platform, I contributed to the robotics research field in the following aspects:(1) I designed and developed a series of eighteen (18) serious VR games and GUIs that could be used for interactive post-stroke rehabilitation training. The VR environment, together with the exoskeleton robot, provides patients and physical therapists a quantitative rehabilitation training platform with capability in real-time human performance data collection and analysis.(2) To provide better compliant control, my colleagues and I proposed and implemented two new admittance controllers, based on the work done by previous research group alumni. Both the hyper parameter-based and Kalman Filter-based admittance controllers have satisfactory heuristic performance, and the latter is more promising in future adaptation. Unlike many other upper-limb exoskeletons, our current system utilizes force and torque (F/T) sensors and position encoders only, no surface electromyography (sEMG) signals are used. It brings convenience to practical use, as well as technical challenges.(3) To provide better AAN control, which is still not well understood in the academia, I worked out a redundant version of modified dynamic manipulability ellipsoid (DME) model to propose an Arm Postural Stability Index (APSI) to quantify the difficulty heterogeneity of the 3D Cartesian workspace. The theoretical framework could be used to teach the exoskeleton where and when to provide assistance, and to guide the virtual reality where to add new minimal challenges to stroke patients. To the best of my knowledge, it is also for the first time that human arm redundancy resolution was investigated when arm gravity is considered.(4) For the first time, my colleagues and I have done a pilot study on asymmetric dual-arm training using the exoskeleton system on one (1) post-stroke patient. The exoskeleton on the healthy side could trigger assistance for that on the affected side, and validates that the current mechanism/control is eligible for asymmetric dual-arm training.(5) Other works of mine include: activities of daily living (ADLs) data visualization for VR game difficulty design; human arm synergy modeling; dual-arm manipulation taxonomy classification (on-going work).
520 $a 在美国每年有大约80万新的中风病例，其中相当多的病人承受着上肢神经肌肉障碍带来的痛苦，包括但不限于肌无力，肌肉痉挛，以及反常的协同作用。基于神经可塑性，病人通常有康复(达到某种程度)的潜力，物理治疗的介入帮助加速他们的康复。但是，很多病人在卒中后无法承担高昂的理疗费用，因此失去了康复的机会。机器人辅助康复因为有着以下不可比拟的优点，可能会成为一个解决方案：（1）全天候24小时都可以对人上肢的重力补偿；（2）多关节运动的协调/矫正，这对于人类理疗师来说是难以实施的；（3）关节空间或者任务空间的双臂训练；（4）一个可以给出指令，提供辅助，施加阻力，并且实时收集运动学、动力学、生物力学数据的量化平台；（5）潜在的训练疗程个性化，等等。虽然有这些潜在的优点，在康复机器人领域，仍然存在这一些悬而未决的开放性问题。我对其中的这些问题尤其感兴趣：（1）高维多关节协作下的柔性控制；（2）基于模型/不基于模型的需即辅助控制；（3）对称和非对称的双臂训练；（4）系统集成，例如虚拟现实下的严肃游戏和图形用户界面设计和开发。我们的第四代双臂/手机器人外骨骼系统，代号EXO-UL8，每侧各有7个臂自由度和1个可以让手开合的单自由度手爪。在这个研究平台上开发功能的同时，我对机器人研究领域做出了以下各方面的贡献：（1）我设计开发了一系列共18个严肃虚拟现实游戏和配套的图形用户界面，可以用于交互式中风康复训练。这套虚拟现实环境和外骨骼机器人一起为中风病患和理疗师提供了一个具有人体表现数据收集分析能力的量化康复训练平台。（2）为了提供更好的柔性控制，我和我的同事基于实验室前人的工作，提出并实现了两种新的导纳控制器。不论是基于超参数的导纳控制器，还是基于卡尔曼滤波的导纳控制器，都有着令人满意的全局表现，但后者可能会在将来的优化中展示出更好的自适应性。不同于其他的上肢外骨骼系统，我们现有的系统采用了力/力矩传感器以及位置编码器而不是表面肌电信号。这给实际使用带来了便利，但技术上是非常具有挑战性的。（3）需即辅助控制在学界还没有得到完全理解，为了提供更好的需即辅助控制，我研发出冗余态的动力可操作性椭球(DME)模型并提出了一个手臂姿态稳定性指数(APSI)，以此来量化三维笛卡尔空间下的难度异构程度。这套理论框架可以用来教会外骨骼何时何处提供辅助，并给虚拟现实下的最小新增挑战提供指导。据我所知，这项工作亦首次研究了在考虑手臂自重情况下人臂冗余的解决。（4）我和我的同事在一位中风病人身上测试了基于外骨骼系统的非对称双臂训练。健侧的机械外骨骼可以触发患侧的外骨骼辅助，证实了目前的机构和控制足以胜任更多的非对称双臂训练。（5）其他的工作包括：用于指导虚拟游戏难度设计的日常生活数据可视化；人手臂协同建模；双臂操作分类图谱的构建，等等。.
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